Connecting Your Data to Discover Semantic Relations

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digital circuit board of AI and knowledge management

Successful business operations are defined by intelligent decisions that come from actionable insights. Discovering how certain entities fit together to optimize profits and create efficient working environments is key to any thriving organization. This was the case before technology, and still is today. Before people even heard of the term “artificial intelligence”, businesses still needed to distinguish correlations and draw conclusions from them. Many times, actions taken would not go as planned because these findings were not based on the entirety of one’s data.  

Today, everything exists in digital databases and data sources. Using artificial intelligence to connect information from all datasets and silos allows enterprises to automatically extract meaning from their information – information that was once invisible to them.

The exciting journey of receiving actionable and personalized insights is just beginning and taking advantage of smart solutions that help your workforce see what they need is a path worth taking for all business leaders.

Discovering Key Correlations Within Your Organization

Semantic relations focus on the meaning of different correlations rather than just sharing that two or more different subjects are related to one another. The extracted relationships are put into context for the exact use case or project the user is working on.

A great example of this is when an employee needs a resource or someone to consult on a certain project. With many specialized areas existing throughout an entire company, people find themselves asking who the best person to contact about a subject matter is all the time, but never seem to have the answer.

Connecting data takes the guessing and endless hunt for information out of the equation. By connecting data from marketing content like blogs and whitepapers, sales slides and decks, and customer support tickets, machine learning can establish who the true expert is for any given topic or question. Machine learning will learn who authored each article, produced each sales demo, or solved each support ticket and show the user who has the most experience with a subject matter – all highlighted in an easily digestible and graphical format.

The Use Case for Research & Development

Use cases for connecting data to discover semantic relations are practically endless. Another prime use case for this technology exists in the R&D space. Machine learning can recognize the best partners for research and development. To launch a successful company or make proper tweaks to your product, understanding what needs to be fixed is critical. What is the problem? What is our organization’s solution? Recognizing the problem and finding hidden patterns to come up with innovative solutions is a must in this area of the business.

Semantic relations help pinpoint the top R&D partners who are experts in their field similar to how it recognizes experts internally in the previous example.

Overall, understanding your data and attaching valuable meaning to it leads to a much higher focused R&D process.

Endless Value to your Business

A lot of executives think that they know every single thing going on in their business. With the mountains of data being created every single day by various departments, I urge them to think again. You cannot know what you cannot see and unless you are using a solution, like an insight engine, there may be key information and relationships within your business that is currently slipping through the cracks and not being leveraged. Using AI as a smart partner lets managers and entire workforces stay on top of their data. By working 24/7 behind the scenes, AI is prepared to offer insights whenever you need a piece of knowledge.

Do you need to find an expert at your company? Do you need the most qualified R&D partner? Whatever it is, the ability to see patterns from every corner of your business can transform the way you work forever.

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